Humanoid Space Companion - Senior Project

System Specifications

• Processor / Compute: NVIDIA Jetson Orin Nano (AI inference + high-level control)

• Microcontroller: STM32 (low-level motor control & sensor interfacing)

• Actuators: High-torque servo motors for hip, knee, and ankle joints

• Sensors:

  • 9-DOF IMU (accelerometer, gyroscope, magnetometer)

  • Force/pressure sensors for ground contact detection

• Control System:

  • Low-level PID loops for joint actuation

  • Sensor fusion via extended Kalman filter for orientation & balance

  • AI-based gait generation and stabilization policies (trained offline, deployed onboard)

• Power: Li-Po battery system with regulated 5V/12V rails

• Dimensions: Approx. 0.6 m tall, 4–5 kg

• Interfaces: UART, I²C, SPI, USB, Wi-Fi for debugging/telemetry

Design & Architecture

Results & Lessons Learned

Results (Expected)

• Locomotion & Balance: Demonstrated stable walking gait and real-time recovery from small disturbances.

• Facial Expression Recognition: Onboard Jetson runs deep learning models for real-time facial expression recognition, enabling the robot to interpret human emotional states (e.g., happy, sad, neutral).

• Task Handling: Executes predefined tasks (e.g., object pickup with gripper, environmental monitoring, or simple assistance routines).

• Interactive Companion Behavior: Responds to verbal or gesture-based commands, adjusting behavior based on context.

• Space Application Tie-In: Designed as a prototype for a “space companion” concept — capable of monitoring crew health cues (via expressions), providing reminders or alerts, and assisting with routine tasks in constrained environments like spacecraft.
  
  

Lessons Learned (Ongoing)

• Human-Robot Interaction: Training reliable facial expression recognition under varying lighting conditions and with limited computing resources is challenging.
  
  

• AI + Embedded Integration: Balancing GPU-intensive inference on Jetson with real-time actuation on STM32 requires careful task scheduling and efficient communication.
  
  

• Task Autonomy: Mapping “high-level commands” (like follow me or pick this up) to low-level motor actuation requires layered control and state machines.
  
  

• Space Context Constraints: Designing for potential use in spacecraft highlights the importance of compact design, energy efficiency, and robustness to disturbances.
  
  

• Iterative Testing: Incremental validation (sensors → actuation → perception → interaction) is critical to avoid compounding errors during integration.

Documentation